class_docstr: In `PTR <https://arxiv.org/pdf/2105.11259.pdf>`_, each prompt has more than one ``<mask>`` tokens.
  Different ``<mask>`` tokens have different label words.
  The final label is predicted jointly by these label words using logic rules.
  
  Args:
      tokenizer (:obj:`PreTrainedTokenizer`): A tokenizer to appoint the vocabulary and the tokenization strategy.
      classes (:obj:`Sequence[str]`): A sequence of classes that need to be projected.
      label_words (:obj:`Union[Sequence[Sequence[str]], Mapping[str, Sequence[str]]]`, optional): The label words that are projected by the labels.
class_docstr_tok: ["In","`","PTR","<","https",":","\/\/arxiv.org\/pdf\/2105.11259.pdf",">","`","_",",","each","prompt","has","more","than","one","``","<","mask",">","``","tokens",".","Different","``","<","mask",">","``","tokens","have","different","label","words",".","The","final","label","is","predicted","jointly","by","these","label","words","using","logic","rules",".","Args",":","tokenizer","(",":","obj",":","`","PreTrainedTokenizer","`",")",":","A","tokenizer","to","appoint","the","vocabulary","and","the","tokenization","strategy",".","classes","(",":","obj",":","`","Sequence","[","str","]","`",")",":","A","sequence","of","classes","that","need","to","be","projected",".","label_words","(",":","obj",":","`","Union","[","Sequence","[","Sequence","[","str","]","]",",","Mapping","[","str",",","Sequence","[","str","]","]","]","`",",","optional",")",":","The","label","words","that","are","projected","by","the","labels","."]
code: def __init__(
      self,
      tokenizer: PreTrainedTokenizer,
      classes: Sequence[str] = None,
      num_classes: Optional[int] = None,
      label_words: Optional[
          Union[Sequence[Sequence[str]], Mapping[str, Sequence[str]]]
      ] = None,
  ):
      super().__init__(
          tokenizer=tokenizer, classes=classes, num_classes=num_classes
      )
      self.label_words = label_words
  
file_path: OpenPrompt/openprompt/prompts/ptr_prompts.py
imports: import json
  from openprompt.data_utils import InputFeatures
  import os
  import torch
  from torch import nn
  from typing import *
  from transformers import PreTrainedModel
  from transformers.tokenization_utils import PreTrainedTokenizer
  from openprompt import Verbalizer
  from openprompt.prompts import One2oneVerbalizer, PtuningTemplate
label_desc: Use image node_id 1 to create a new PTRVerbalizer object from inherited base classes: Verbalizer with example: obj = PTRVerbalizer(tokenizer, classes, num_classes, label_words)